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Numerical Analysis Lab Note #4 Matrix Related Functions and Logical Operators in Matlab

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Numerical Analysis Lab Note #4 Matrix Related Functions and Logical Operators in Matlab Numerical Analysis Lab Note #4 Matrix Related Functions and Logical Operators in Matlab CONTENTS 1. Matrix Generating Functions 2. Matrix Related Function 3. Logical Operators =================================================================== 1. Matrix Generating Functions: ONES(N,M), ZEROS(N,M), EYE(N), RAND(N,M), MAGIC(N), PASCAL(N), HILB(N), TOEPLITZ, HANKEL, KRON. >> help ones ONES Ones array. ONES(N) is an N-by-N matrix of ones. ONES(M,N) or ONES([M,N]) is an M-by-N matrix of ones. ONES(M,N,P,...) or ONES([M N P ...]) is an M-by-N-by-P-by-... array of ones. ONES(SIZE(A)) is the same size as A and all ones. ONES with no arguments is the scalar 1. ONES(M,N,...,CLASSNAME) or ONES([M,N,...],CLASSNAME) is an M-by-N-by-... array of ones of class CLASSNAME. Note: The size inputs M, N, and P... should be nonnegative integers. Negative integers are treated as 0. Example: x = ones(2,3,'int8'); 1 >> help zeros ZEROS Zeros array. ZEROS(N) is an N-by-N matrix of zeros. ZEROS(M,N) or ZEROS([M,N]) is an M-by-N matrix of zeros. ZEROS(M,N,P,...) or ZEROS([M N P ...]) is an M-by-N-by-P-by-... array of zeros. ZEROS(SIZE(A)) is the same size as A and all zeros. ZEROS with no arguments is the scalar 0. ZEROS(M,N,...,CLASSNAME) or ZEROS([M,N,...],CLASSNAME) is an M-by-N-by-... array of zeros of class CLASSNAME. Note: The size inputs M, N, and P... should be nonnegative integers. Negative integers are treated as 0. Example: x = zeros(2,3,'int8'); >> help eye EYE Identity matrix. EYE(N) is the N-by-N identity matrix. EYE(M,N) or EYE([M,N]) is an M-by-N matrix with 1's on the diagonal and zeros elsewhere. EYE(SIZE(A)) is the same size as A. EYE with no arguments is the scalar 1. EYE(M,N,CLASSNAME) or EYE([M,N],CLASSNAME) is an M-by-N matrix with 1's of class CLASSNAME on the diagonal and zeros elsewhere. Note: The size inputs M and N should be nonnegative integers. Negative integers are treated as 0. Example: x = eye(2,3,'int8'); >> help rand RAND Uniformly distributed pseudorandom numbers. R = RAND(N) returns an N-by-N matrix containing pseudorandom values drawn from the standard uniform distribution on the open interval(0,1). RAND(M,N) or RAND([M,N]) returns an M-by-N matrix. RAND(M,N,P,...) or RAND([M,N,P,...]) returns an M-by-N-by-P-by-... array. RAND returns a scalar. RAND(SIZE(A)) returns an array the same size as A. 2 Note: The size inputs M, N, P, ... should be nonnegative integers. Negative integers are treated as 0. R = RAND(..., 'double') or R = RAND(..., 'single') returns an array of uniform values of the specified class. Examples: Generate values from the uniform distribution on the interval [a, b]. r = a + (b-a).*rand(100,1); Generate integer values from the uniform distribution on the set 1:n. r = randi(100,1); Save the current state of the default stream, generate 5 values, restore the state, and repeat the sequence. defaultStream = RandStream.getDefaultStream; savedState = defaultStream.State; u1 = rand(1,5) defaultStream.State = savedState; u2 = rand(1,5) % contains exactly the same values as u1 Replace the default stream with a stream whose seed is based on CLOCK, so RAND will return different values in different MATLAB sessions. NOTE: It is usually not desirable to do this more than once per MATLAB session. RandStream.setDefaultStream(RandStream('mt19937ar','seed',sum(100*clock))); rand(1,5) >> help magic MAGIC Magic square. MAGIC(N) is an N-by-N matrix constructed from the integers 1 through N^2 with equal row, column, and diagonal sums. Produces valid magic squares for all N > 0 except N = 2. >> magic(4) ans = 16 2 3 13 5 11 10 8 9 7 6 12 4 14 15 1 >> help pascal PASCAL Pascal matrix. PASCAL(N) is the Pascal matrix of order N: a symmetric positive definite matrix with integer entries, made up from Pascal's triangle. Its inverse has integer entries. 3 PASCAL(N).^r is symmetric positive semidefinite for all nonnegative r. PASCAL(N,1) is the lower triangular Cholesky factor (up to signs of columns) of the Pascal matrix. It is involutory (is its own inverse). PASCAL(N,2) is a rotated version of PASCAL(N,1), with sign flipped if N is even, which is a cube root of the identity. PASCAL(N,CLASSNAME) or PASCAL(N,K,CLASSNAME) produces a matrix of class CLASSNAME. CLASSNAME must be either 'single' or 'double' (the default). Reference page in Help browser doc pascal >> pascal(3) ans = 1 1 1 1 2 3 1 3 6 >> pascal(5) ans = 1 1 1 1 1 1 2 3 4 5 1 3 6 10 15 1 4 10 20 35 1 5 15 35 70 >> help hilb HILB Hilbert matrix. HILB(N) is the N by N matrix with elements 1/(i+j-1), which is a famous example of a badly conditioned matrix. See INVHILB for the exact inverse. HILB(N,CLASSNAME) produces a matrix of class CLASSNAME. CLASSNAME must be either 'single' or 'double' (the default). This is also a good example of efficient MATLAB programming style where conventional FOR or DO loops are replaced by vectorized statements. This approach is faster, but uses more storage. 4 See also invhilb. Reference page in Help browser doc hilb >> hilb(3) ans = 1.0000 0.5000 0.3333 0.5000 0.3333 0.2500 0.3333 0.2500 0.2000 EXERCISE: >> help toeplitz >> help hankel >> help kron =================================================================== 2. Matrix Related Functions: SIZE, LENGTH, DET, RANK, EIG, COND, NORM SUM, DIFF, PROD. MAX, MIN, ABS. >> help size SIZE Size of array. D = SIZE(X), for M-by-N matrix X, returns the two-element row vector D = [M,N] containing the number of rows and columns in the matrix. For N-D arrays, SIZE(X) returns a 1-by-N vector of dimension lengths. Trailing singleton dimensions are ignored. [M,N] = SIZE(X) for matrix X, returns the number of rows and columns in X as separate output variables. [M1,M2,M3,...,MN] = SIZE(X) for N>1 returns the sizes of the first N dimensions of the array X. If the number of output arguments N does not equal NDIMS(X), then for: N > NDIMS(X), SIZE returns ones in the "extra" variables, i.e., outputs NDIMS(X)+1 through N. N < NDIMS(X), MN contains the product of the sizes of dimensions N 5 through NDIMS(X). M = SIZE(X,DIM) returns the length of the dimension specified by the scalar DIM. For example, SIZE(X,1) returns the number of rows. If DIM > NDIMS(X), M will be 1. When SIZE is applied to a Java array, the number of rows returned is the length of the Java array and the number of columns is always 1. When SIZE is applied to a Java array of arrays, the result describes only the top level array in the array of arrays. Example: If X = rand(2,3,4); then d = size(X) returns d = [2 3 4] [m1,m2,m3,m4] = size(X) returns m1 = 2, m2 = 3, m3 = 4, m4 = 1 [m,n] = size(X) returns m = 2, n = 12 m2 = size(X,2) returns m2 = 3 See also length, ndims, numel. >>help length LENGTH Length of vector. LENGTH(X) returns the length of vector X. It is equivalent to MAX(SIZE(X)) for non-empty arrays and 0 for empty ones. >> help det DET Determinant. DET(X) is the determinant of the square matrix X. Use COND instead of DET to test for matrix singularity. See also cond. >> help rank RANK Matrix rank. RANK(A) provides an estimate of the number of linearly independent rows or columns of a matrix A. RANK(A,tol) is the number of singular values of A that are larger than tol. RANK(A) uses the default tol = max(size(A)) * eps(norm(A)). Class support for input A: float: double, single >> help eig 6 EIG Eigenvalues and eigenvectors. E = EIG(X) is a vector containing the eigenvalues of a square matrix X. [V,D] = EIG(X) produces a diagonal matrix D of eigenvalues and a full matrix V whose columns are the corresponding eigenvectors so that X*V = V*D. [V,D] = EIG(X,'nobalance') performs the computation with balancing disabled, which sometimes gives more accurate results for certain problems with unusual scaling. If X is symmetric, EIG(X,'nobalance') is ignored since X is already balanced. E = EIG(A,B) is a vector containing the generalized eigenvalues of square matrices A and B. [V,D] = EIG(A,B) produces a diagonal matrix D of generalized eigenvalues and a full matrix V whose columns are the corresponding eigenvectors so that A*V = B*V*D. EIG(A,B,'chol') is the same as EIG(A,B) for symmetric A and symmetric positive definite B. It computes the generalized eigenvalues of A and B using the Cholesky factorization of B. EIG(A,B,'qz') ignores the symmetry of A and B and uses the QZ algorithm. In general, the two algorithms return the same result, however using the QZ algorithm may be more stable for certain problems. The flag is ignored when A and B are not symmetric. See also condeig, eigs, ordeig.
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